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Mar 25, 2013

New Biomarkers Could Offer Vital Clues for Cancer Research

European funding for cancer research has led to some interesting results in recent years.

A unique strategic research initiative in Sweden, called BioCARE (Biomarkers in Cancer Medicine), currently underway at the Universities of Lund and Gothenburg in Sweden, is taking an in-depth look at the role of biomarkers in cancer research. A biomarker is any characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.

Although the term biomarker is relatively new, these characteristics have been used in preclinical research and clinical diagnosis for a considerable time. For example, blood pressure is now often used to determine the risk of stroke. It is also widely known that cholesterol values can act as a biomarker (and risk indicator) for coronary and vascular disease, that C-reactive protein (CRP) is a biomarker for inflammation and prostate-specific antigen (PSA) for prostate cancer.

When examined in molecular terms, these biomarkers describe “the subset of markers that might be discovered using genomics, proteomics technologies, or imaging technologies”. Viewed in this way, biomarkers could have a major role to play in medicinal biology, as they can help with early diagnosis, disease prevention, drug target identification, drug response, and more. As such, gene-based biomarkers are widely considered to be an effective way of studying human disease.

Using Biomarkers within Medicinal Biology

For decades, biomedical scientists have been looking for new ways to diagnose cancers at an early, curable stage and also to select the optimal therapy for individual patients. At the moment, cancer treatments are effective in only some of the patients undergoing therapy, and many cancer patients are still being diagnosed too late, once the cancer is already too far advanced. Despite these challenges, researchers are now exploring how unique biomarkers could help to improve the outcome for people with cancer by enhancing detection and treatment approaches.

When identified at an early stage, biomarkers can provide an important tool for diagnosing disease types and stages, predicting the outcomes of different therapies, as well as monitoring pharmaceutical development. As such, the BioCARE project is focusing on different tumors in order to pursue the identification and validation of biomarkers as both diagnostic and therapeutic targets, and also to facilitate the identification of cancer subpopulations based on clinical behavior and treatment response in tumors. BioCARE has a strong focus on bringing new knowledge into the healthcare sector.

To this end, BioCARE is using cutting-edge omics platforms, extensive biobanks, and close collaboration between senior cancer researchers to identify and analyze new biomarkers related to cancer, along with the complex networks that these biomarkers inhabit. As a result, the BioCARE program hopes to have a major impact on the way cancer is diagnosed, treated, and managed in the next five to ten years.

The Role of Genomic Signatures

To use a very simple analogy, genomic signatures can be used to identify cancer cells in the way that a flag can identify a particular country. Although each flag is composed of several distinct colors and shapes, when these items are put together and arranged in a certain way, they form a distinctive pattern—or signature—that helps to distinguish one flag from another.

When studying genetic data, researchers are essentially looking for recognizable patterns like these. However, they are faced with the challenge of trying to extract these “shapes” from huge arrays of genes, proteins, and/or RNA molecules.

What comes out of this analysis is an incredible, almost impossible to imagine amount of data. If it were printed out, these findings would run to thousands of pages. As such, it has become increasingly difficult to identify which genes are relevant and to what degree, especially when working with tens of thousands of data points being generated by hundreds of different patients.

To make matters even more challenging, research groups working in this area typically consist of a collection of highly trained specialists, each of whom has a unique technical skill. As a result, each individual person on the research team—whether a pathologist, molecular biologist, and/or biostatistician—is often so specialized that none of them fully understands exactly what his or her colleagues are doing.

Software Supports Easier Analysis

Click Image To Enlarge +

Recent developments in software allow for the creation of 3D graphics that can be very useful for people doing biomarker research.

Despite these challenges, it is absolutely essential for scientists working in this area to capture, explore, and analyze this vast amount of data effectively, since this information is vital if they are to apply their findings to real-world conditions.

Fortunately, the latest software in this area is now helping to accelerate and facilitate the understanding of both the context and relationships of the information contained within large datasets by displaying them graphically, in real time. The simplicity of this interaction now makes it possible for researchers to work with powerful and statistical analysis in entirely new ways. Not only that, but faster analysis means that scientists often have more time to test more creative theories, which in turn leads to better research results.

When analyzing this research data, scientists often rely on principal component analysis (PCA), a method that can be used to project high dimensional data down to lower dimensions. By using PCA, specialist software can then be used to plot the lower dimension data produced via PCA onto a two-dimensional computer screen, so that full-color 3D images can be rotated and examined with the naked eye more easily. The application can then be used to manipulate the different PCA-plots—interactively and in real time—complete with all annotations and other integrated links, as well as a number of powerful statistical functions such as false discovery rates (FDR) and p-values.

Can Data Visualization Help to Reveal New Biomarkers?

When used during cancer research, the ability to visualize this data in 3D represents a very powerful tool for scientists looking for new biomarkers, since the human brain is very good at detecting structures and patterns. As such, this approach to information visualization offers a way to transform raw data into a comprehensible graphical format, so that scientists can make decisions based on information that they can understand more easily.

The 3D graphics that can be created with the latest software in this area are both stunning and extremely useful. For researchers gathering measure points from thousands of biological variables, for example, this level of data visualization can produce results in seconds, even if the researcher isn’t a trained statistician.

Likewise, if the data has batch effects—or is paired with unwanted dependencies—these variables can be removed with just a few mouse clicks, so that the results can be delivered and analyzed in real time. It’s therefore now possible to investigate the output of large clinical trials very quickly, and therefore test different hypotheses and explore alternative scenarios within seconds. As a result, scientists looking for new biomarkers can now drastically shorten their analysis time when attempting to identify relevant structures in their data.

The Future

It’s a known fact that the incidence of cancer increases with age, which means that the clinical management of cancer will remain a major challenge for the scientific and medical communities for years to come. It will therefore be vital to identify new strategies for the management and treatment of cancer, and to study the key role of biomarkers this area.

Over the last three years, a major effort has been made to develop sophisticated software that is not only extremely powerful, but which can also help scientists to explore and analyze the high-dimensional data sets interactively and in real time. With this approach, researchers are already able to analyze and explore extremely large datasets—even those with more than 100 million data samples—on a regular PC.

More important still, the latest, most intuitive software in this field will allow the actual researchers involved—the people with the most biological insight—to study the data and to look for patterns and structures, without having to be a statistics or computer expert. As a result, this kind of software will help to provide unprecedented insight for cancer research programs like BioCARE, and will therefore play an invaluable role in the ongoing study of biomarkers.

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